Analysis(DE,Plot)

library(dplyr)
library(knitr)
library(limma)
library(DT)
library(readr)
library(knitr)
library(tidyr)
library(ggplot2)
library(ggcorrplot)

3. Variable Selection Rationale

We selected SLEDAI, C3, and C4 as our core clinical indicators for risk grouping and downstream differential expression analysis based on their biological importance and widespread use in lupus research and clinical practice.

The table below summarizes the full names, biological functions, cutoff thresholds, and supporting references of each variable:

Variable	Full Name	Biological Role	Cutoff Threshold	Reference
SLEDAI	Systemic Lupus Erythematosus Disease Activity Index	Measures lupus disease activity. Higher scores indicate more active disease.	≥ 10 (High Risk)	Bombardier et al., 1992; Petri et al., 2005
C3	Complement Component 3	Immune protein; lower levels indicate increased immune complex activation.	< 0.9 g/L (High Risk)	Walport, 2001; Yang et al., 2020
C4	Complement Component 4	Works with C3; its reduction also reflects lupus disease activity.	< 0.1 g/L (High Risk)	Walport, 2001; Yang et al., 2020

4. Data Analysis

4.1 Simple EDA

Read the data again and check if it was successfully pulled. Perform data composition and clean up necessary NA values for simple answers.

# View NA value
na_summary <- data.frame(
  Variable = c("SLEDAI", "C3", "C4"),
  NA_Count = c(
    sum(is.na(pheno_clean$sledai_at_baseline)),
    sum(is.na(pheno_clean$c3)),
    sum(is.na(pheno_clean$c4))
  )
)


datatable(na_summary,
          caption = "NA Count Summary for Clinical Variables",
          options = list(pageLength = 5, autoWidth = TRUE))

#Replace NA value with median to ensure dimensional consistency.
median_sledai <- median(pheno_clean$sledai_at_baseline, na.rm = TRUE)
median_c3 <- median(pheno_clean$c3, na.rm = TRUE)
median_c4 <- median(pheno_clean$c4, na.rm = TRUE)

pheno_clean <- pheno_clean %>%
  mutate(
    sledai_at_baseline = ifelse(is.na(sledai_at_baseline), median_sledai, sledai_at_baseline),
    c3 = ifelse(is.na(c3), median_c3, c3),
    c4 = ifelse(is.na(c4), median_c4, c4)
  )

summary_df <- as.data.frame(summary(pheno_clean))

datatable(summary_df,
          caption = "Summary Statistics After NA Imputation",
          options = list(pageLength = 10, autoWidth = TRUE))

4.1.2Variable distribution

We created histograms to understand the underlying distribution of each clinical variable. It helps us determine whether the data is skewed or symmetrical, and whether a threshold-based grouping is reasonable.

pheno_long <- pheno_clean %>%
  select(SLEDAI = sledai_at_baseline, C3 = c3, C4 = c4) %>%
  pivot_longer(cols = everything(), names_to = "Variable", values_to = "Value")

ggplot(pheno_long, aes(x = Value)) +
  geom_histogram(bins = 30, fill = "#0073C2FF", color = "white") +
  facet_wrap(~Variable, scales = "free") +
  labs(title = "Distribution of Clinical Variables",
       x = "Value",
       y = "Frequency") +
  theme_minimal()

Boxplot of Clinical Variables

We can see that SLEDAI distinguishes the activity levels between samples very clearly. It is one of the core indicators of group modeling

ggplot(pheno_long, aes(x = "", y = Value)) +
  geom_boxplot(fill = "#f9844a") +
  facet_wrap(~Variable, scales = "free_y") +  
  labs(title = "Boxplot of Clinical Variables ",
       x = "",
       y = "Value") +
  theme_minimal()

4.1.3Correlation Between Clinical Variables

C3 and C4: strongly positively correlated (r=0.71), biologically consistent with the immune complement system.

SLEDAI and C3: Negative correlation (r=-0.31)

SLEDAI and C4: Negative correlation (r=-0.27)

It can be seen that there is no multicollinearity among the three variables, which can be used together for grouping and modeling. And consistent with the literature, the higher the activity of SLE, the lower the complement.

cor_vars <- pheno_clean %>%
  select(SLEDAI = sledai_at_baseline, C3 = c3, C4 = c4)

cor_matrix <- cor(cor_vars, use = "complete.obs", method = "pearson")

ggcorrplot(cor_matrix, 
           lab = TRUE,
           type = "lower", 
           lab_size = 4,
           title = "Correlation Matrix of Clinical Variables",
           colors = c("#B2182B", "white", "#2166AC"))

More Display - Personnel Variables Display

The sample population is predominantly female (1620 out of 1750), which is consistent with the epidemiology of systemic lupus erythematosus (SLE), a disease known to disproportionately affect women. This supports the clinical representativeness of the dataset and suggests that sex may serve as a useful covariate in downstream modeling.

if("sex" %in% names(pheno_clean)) {
  sex_table <- as.data.frame(table(pheno_clean$sex))
  colnames(sex_table) <- c("Sex", "Count")
  
  datatable(sex_table,
            caption = "Sex Distribution in the Sample",
            options = list(pageLength = 5, autoWidth = TRUE))
}

if("age" %in% names(pheno_clean)) {
  ggplot(pheno_clean, aes(x = age)) +
    geom_histogram(bins = 30, fill = "#00AFBB", color = "white") +
    labs(title = "Age Distribution", x = "Age", y = "Frequency") +
    theme_minimal()
}

4.2 Difference Analysis

4.2.1Exploration of risk grouping

According to the conclusions of Anian’s data cleaning and the references in the paper. Risk grouping is performed for the three important indicators of SLE activity index, C3, and C4.

#Create risk classification based on different clinical indicators
pheno_clean$risk_group_sledai <- ifelse(
  pheno_clean$sledai_at_baseline >= 10, "HighRisk_SLEDAI", "LowRisk_SLEDAI"
)
pheno_clean$risk_group_c3 <- ifelse(
  pheno_clean$c3 < 0.9, "HighRisk_C3", "LowRisk_C3"
)
pheno_clean$risk_group_c4 <- ifelse(
  pheno_clean$c4 < 0.1, "HighRisk_C4", "LowRisk_C4"
)

#Calculate the sample size for each risk group
df_sledai = as_tibble(table(pheno_clean$risk_group_sledai), .name_repair = "minimal")
df_c3 = as_tibble(table(pheno_clean$risk_group_c3), .name_repair = "minimal")
df_c4 = as_tibble(table(pheno_clean$risk_group_c4), .name_repair = "minimal")


colnames(df_sledai) = c("Group", "Count_SLEDAI")
colnames(df_c3) = c("Group", "Count_C3")
colnames(df_c4) = c("Group", "Count_C4")


df_combined_sledai_c3 = full_join(df_sledai, df_c3, by = "Group")
df_combined = full_join(df_combined_sledai_c3, df_c4, by = "Group")

kable(df_combined, caption = "Risk Group Statistics Based on SLEDAI, C3, and C4 Levels")

Risk Group Statistics Based on SLEDAI, C3, and C4 Levels

Group	Count_SLEDAI	Count_C3	Count_C4
HighRisk_SLEDAI	1073	NA	NA
LowRisk_SLEDAI	747	NA	NA
HighRisk_C3	NA	650	NA
LowRisk_C3	NA	1170	NA
HighRisk_C4	NA	NA	384
LowRisk_C4	NA	NA	1436

4.2.2 Differential analysis of SLEDAI risk grouping

The selection of threshold is based on data cleaning and the support of geo sourced literature. 2.And select significant genes based on the significance level of p<0.05.

threshold_sledai <- 10
pheno_clean$risk_group_SLEDAI <- ifelse(pheno_clean$sledai_at_baseline >= threshold_sledai, "HighRisk_SLEDAI", "LowRisk_SLEDAI")


design_sledai <- model.matrix(~ pheno_clean$risk_group_SLEDAI)

#Fit limma model
fit_sledai <- lmFit(expr_df_matched, design_sledai)

#Applying eBayes method
fit_sledai <- eBayes(fit_sledai)

#Extract differentially expressed genes
results_sledai <- topTable(fit_sledai, coef=2, n=Inf)  
results_sledai_filtered <- results_sledai[results_sledai$adj.P.Val < 0.05, ]  

#Create an interactive table using the DT package
datatable(round(results_sledai_filtered, 2), 
          options = list(pageLength = 10, autoWidth = TRUE),
          caption = "Significant Differentially Expressed Genes in SLEDAI (p < 0.05)")

4.2.3 Differential analysis of C3 risk grouping

Process, threshold setting, and significance level setting are consistent with 4.2.1

threshold_c3 <- 0.9

pheno_clean$risk_group_C3 <- ifelse(pheno_clean$c3 < threshold_c3, "HighRisk_C3", "LowRisk_C3")


design_c3 <- model.matrix(~ pheno_clean$risk_group_C3)

fit_c3 <- lmFit(expr_df_matched, design_c3)

fit_c3 <- eBayes(fit_c3)


results_c3 <- topTable(fit_c3, coef=2, n=Inf)  
results_c3_filtered <- results_c3[results_c3$adj.P.Val < 0.05, ]  


datatable(round(results_c3_filtered, 2), 
          options = list(pageLength = 10, autoWidth = TRUE),
          caption = "Significant Differentially Expressed Genes in C3 Analysis (p < 0.05)")

4.2.4 Differential analysis of C4 risk grouping

Process, threshold setting, and significance level setting are consistent with 4.2.1

# 假设C4得分阈值是0.1
threshold_c4 <- 0.1

# 创建风险分组列
pheno_clean$risk_group_C4 <- ifelse(pheno_clean$c4 < threshold_c4, "HighRisk_C4", "LowRisk_C4")

# 为 C4 风险分组设置设计矩阵
design_c4 <- model.matrix(~ pheno_clean$risk_group_C4)

# 拟合 limma 模型
fit_c4 <- lmFit(expr_df_matched, design_c4)

# 应用 eBayes 方法
fit_c4 <- eBayes(fit_c4)

# 提取差异表达的基因
results_c4 <- topTable(fit_c4, coef=2, n=Inf)  # 提取所有基因
results_c4_filtered <- results_c4[results_c4$adj.P.Val < 0.05, ]  # 筛选 p 值小于 0.05 的基因

# 使用 DT 包来创建一个交互式表格
datatable(round(results_c4_filtered, 2), 
          options = list(pageLength = 10, autoWidth = TRUE),
          caption = "Significant Differentially Expressed Genes in C4 Analysis (p < 0.05)")

Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

4.3 Merge common significant genes

Merge and display the common significant genes of the three risk groups, so that the selected common significant genes under the three high reference indicators will definitely have stronger predictive performance.

#Screen genes with p-values less than 0.05 in each analysis group
significant_sledai <- results_sledai[results_sledai$adj.P.Val < 0.05,]
significant_c3 <- results_c3[results_c3$adj.P.Val < 0.05,]
significant_c4 <- results_c4[results_c4$adj.P.Val < 0.05,]

#Find the intersection using gene names
common_genes <- Reduce(intersect, list(rownames(significant_sledai), 
                                       rownames(significant_c3), 
                                       rownames(significant_c4)))

#Can create a data box containing these common genetic information
common_genes_data <- data.frame(
  Gene = common_genes,
  PVal_SLEDAI = significant_sledai[common_genes, "adj.P.Val", drop = FALSE],
  PVal_C3 = significant_c3[common_genes, "adj.P.Val", drop = FALSE],
  PVal_C4 = significant_c4[common_genes, "adj.P.Val", drop = FALSE]
)


datatable(common_genes_data, 
          options = list(pageLength = 10, autoWidth = TRUE),
          caption = "Common Significant Genes Across SLEDAI, C3, and C4 Analyses")

# 保存筛选后的基因数据到 CSV 文件
write.csv(common_genes_data, "Common_Significant_Genes.csv", row.names = FALSE)

5.Interactive chart display

5.1. Interactive volcano map presentation

To avoid excessive rendering complexity in presenting files, only genes with good performance in common differences were displayed. Avoiding the occurrence of lagging situations.

results_sledai$Gene <- rownames(results_sledai)
results_sledai$Time <- "SLEDAI"

results_c3$Gene <- rownames(results_c3)
results_c3$Time <- "C3"

results_c4$Gene <- rownames(results_c4)
results_c4$Time <- "C4"


volcano_all <- rbind(results_sledai, results_c3, results_c4)


volcano_all$IsCommon <- ifelse(volcano_all$Gene %in% common_genes, "Common", "NotCommon")


volcano_common <- subset(volcano_all, IsCommon == "Common")


library(ggplot2)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

p_common <- ggplot(volcano_common, aes(x = logFC, y = -log10(P.Value))) +
  geom_point(aes(color = Time, text = Gene), size = 1.5, alpha = 0.8) +
  facet_wrap(~ Time) +
  labs(x = "logFC", y = "-log10(p value)", title = "Common Significant Genes") +
  theme_minimal()

Warning in geom_point(aes(color = Time, text = Gene), size = 1.5, alpha = 0.8):
Ignoring unknown aesthetics: text

p_interactive <- ggplotly(p_common, tooltip = "text")
p_interactive

5.2. MA interaction diagram presentation

Presented in the form of MA charts, the data filtering is consistent with volcano charts.

p_ma <- ggplot(volcano_common, aes(x = AveExpr, y = logFC)) +
  geom_point(aes(color = Time, text = Gene), size = 1.5, alpha = 0.8) +
  facet_wrap(~ Time) +
  labs(x = "Average Expression (log2)", y = "log2 Fold Change", title = "MA Plot - Common DEGs") +
  theme_minimal()

Warning in geom_point(aes(color = Time, text = Gene), size = 1.5, alpha = 0.8):
Ignoring unknown aesthetics: text

p_ma_interactive <- ggplotly(p_ma, tooltip = "text")
p_ma_interactive